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Modeling evolution of protein coding...

Pond, Sergei L. Kosakovsky.

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Modeling evolution of protein coding DNA sequences.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Modeling evolution of protein coding DNA sequences./
Author:	Pond, Sergei L. Kosakovsky.
Description:	121 p.
Notes:	Source: Dissertation Abstracts International, Volume: 64-05, Section: B, page: 2258.
Contained By:	Dissertation Abstracts International64-05B.
Subject:	Statistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3090006

Modeling evolution of protein coding DNA sequences.
Pond, Sergei L. Kosakovsky.

Modeling evolution of protein coding DNA sequences. - 121 p.

Source: Dissertation Abstracts International, Volume: 64-05, Section: B, page: 2258.

Thesis (Ph.D.)--The University of Arizona, 2003.

We develop a new class of computationally feasible stochastic models for statistical analysis of genetic sequence evolution and inference of properties of the underlying substitution processes in the context of maximum likelihood framework. Existing models for evolution of protein coding sequences allow site to site variation in non-synonymous substitution rates, but assume that the rate of synonymous substitutions is constant for all sites. New models provide a rigorous statistical framework for testing the hypothesis of synonymous rate constancy, and enable a host of data exploration and analysis tools. For several indicative data sets, the constancy assumption is shown to be violated, and some possible explanations are given. We also present an algorithm for improving efficiency of maximum likelihood evaluations, and discuss <italic> HyPhy</italic>—a user friendly and publicly distributed software implementation of our methods.Subjects--Topical Terms:

517247
Statistics.

Modeling evolution of protein coding DNA sequences.
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035 $a (UnM)AAI3090006
035 $a AAI3090006
040 $a UnM $c UnM
100 1 $a Pond, Sergei L. Kosakovsky. $3 1946044
245 1 0 $a Modeling evolution of protein coding DNA sequences.
300 $a 121 p.
500 $a Source: Dissertation Abstracts International, Volume: 64-05, Section: B, page: 2258.
500 $a Director: Joseph C. Watkins.
502 $a Thesis (Ph.D.)--The University of Arizona, 2003.
520 $a We develop a new class of computationally feasible stochastic models for statistical analysis of genetic sequence evolution and inference of properties of the underlying substitution processes in the context of maximum likelihood framework. Existing models for evolution of protein coding sequences allow site to site variation in non-synonymous substitution rates, but assume that the rate of synonymous substitutions is constant for all sites. New models provide a rigorous statistical framework for testing the hypothesis of synonymous rate constancy, and enable a host of data exploration and analysis tools. For several indicative data sets, the constancy assumption is shown to be violated, and some possible explanations are given. We also present an algorithm for improving efficiency of maximum likelihood evaluations, and discuss <italic> HyPhy</italic>—a user friendly and publicly distributed software implementation of our methods.
590 $a School code: 0009.
650 4 $a Statistics. $3 517247
650 4 $a Biology, Biostatistics. $3 1018416
650 4 $a Biology, Genetics. $3 1017730
690 $a 0463
690 $a 0308
690 $a 0369
710 2 0 $a The University of Arizona. $3 1017508
773 0 $t Dissertation Abstracts International $g 64-05B.
790 1 0 $a Watkins, Joseph C., $e advisor
790 $a 0009
791 $a Ph.D.
792 $a 2003
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3090006